The number of drastic somatic mutations increases in culture-adapted hESCs
We identified the cellular and molecular events that occur during long-term in vitro passage using H9 hESCs with different passage numbers, which were maintained for up to 6 years (Fig. 1a). Genomic variants (i.e., P3 and P4 hESCs) share typical ‘culture-adapted phenotypes’, as previously demonstrated11,12,19,34,35,36 and depicted in Fig. 1a. As these ‘culture-adapted phenotypes’ were manifested in P3 hESCs (but not P2 hESCs) and P4 hESCs with an additional 17q24 gain18 (Supplementary Fig. 1a) over P3 hESCs (with a 20q11.21 gain), closer examination of ‘stepwise variation models for genome hESC instability’ is warranted20,37.
a General scheme of passage-dependent isogenic pairs of hESCs. P1 hESCs: fewer than 50 passages (one-year culture), P2 hESCs: more than 100 passages (two-year culture), P3 hESCs: more than 200 passages (four-year culture), and P4 hESCs: more than 300 passages (culture greater than six years). b Number of somatic single-nucleotide variants (SNVs) detected by whole-genome sequencing of P1, P2, P3 and P4 hESCs and overlaps of somatic SNVs (detected via Mutect2) among those hESCs. The number of missense SNVs is highlighted in blue. c The frequencies of somatic SNVs in intergenic regions, introns, and exons in P2, P3 and P4 hESCs were calculated by dividing the number of somatic SNVs found in each genomic feature by the length of that region. d The number of somatic intron, missense, 5′/3′-UTR, and nonsense SNVs identified in P2, P3, and P4 hESCs. e The chromosomal distribution of somatic SNVs in P2, P3, and P4 hESCs. The abundance of somatic SNVs is presented as a heatmap. f Functional pathways involved in the functions of survival and proliferation in stem cells and mutated in P3 and P4 were analyzed via Qiagen Ingenuity Pathway Analysis, as described in the supplementary methods. g Genes with somatic missense SNVs (highlighted in blue in the first panel of Fig. 1g) were checked against The Cancer Genome Atlas (TCGA) and the Catalog of Somatic Mutations in Cancer (COSMIC)-Tier 1 and -hallmark (HM) databases (highlighted in yellow in the second panel of Fig. 1g). h Somatic missense SNVs in P3 and P4 hESCs were searched against the TP53-directed network and/or COSMIC Tier 1 datasets, and the impact of those mutations was assessed via T scores for the TP53-direct network and C-scores for COSMIC Tier 1 (see the Methods). Finally, the resulting scores were compared with data from the Cancer Cell Line Encyclopedia (CCLE).
We investigated progressive somatic mutations in long-term culture-adapted hESCs by performing WGS on the hESC set and analyzing the genomic distribution of somatic single-nucleotide variants (SNVs) in P2, P3, and P4 hESCs, with P1 as a normal control. Following quality control guidelines (see the Materials and Methods section), 42,722 somatic SNVs were identified. The primary variant types analyzed were C > T, C > A, and T > C (Supplementary Fig. 1b). Somatic SNVs in P3 and P4 hESCs increased by approximately 1.8-fold compared with those in P2 hESCs, which coincides with the significant occurrence of P3- and P4-specific SNVs. (Fig. 1b). This correlation demonstrated an association between the increase in passage number and somatic mutation.
Intergenic, intronic, and exonic region mutation frequencies, as annotated in the GRCh37 genome assembly, were measured and normalized to the base-pair content of each feature class (Fig. 1c, Supplementary Fig. 1b, c). P2 and P3/P4 hESC mutation frequencies significantly differed in introns (approximately 2.53 [P = 2.25 × 10−8] and 2.47 [P = 3.26 × 10−9] times more abundant in P3 and P4 than in P2, respectively) and exons (approximately 4.10 [P = 4.58 × 10−8] and 4.36 [P = 1.07 × 10−8] times more abundant in P3 and P4 than in P2, respectively). Specifically, concerning coding regions, a significant increase in missense and nonsense variants was observed in P3 and P4 (Fig. 1d). Our results indicate that genetic variant accumulation in genic regions during long-term in vitro passaging augments hESC genome instability.
Interestingly, we observed that the somatic mutations were nonrandom; somatic SNVs in chromosomes 1 and 19 were the most prevalent (Fig. 1e and Supplementary Fig. 1d). Notably, recurrent gains are frequently observed on chromosome 138,39. Along with the drastic increase in P3 and P4 hESC mutations, we identified 29 missense mutations, primarily consisting of C > T and C > A variant types (Supplementary Fig. 1c), in 26 genes associated with apoptosis, DNA repair, the G2/M checkpoint (cell cycle), the mitotic spindle, P53, Hedgehog signaling, PI3K signaling, and Rho signaling (Fig. 1f). Among these pathways, PI3K gain and Rho signaling loss also increase the survival benefit of hPSCs according to unbiased genome-wide screening40 or genetic perturbation studies41.
We identified 30 genes with missense SNVs in P3 or P4 hESCs associated with tumorigenesis in The Cancer Genome Atlas (TCGA) (second panel in Fig. 1g). These genes were frequently localized in colon and rectum adenocarcinomas in the TCGA cohort (third panel in Fig. 1g). Additionally, nine of these genes, including TP53, AFF1, BRD4, CREBBP, ERBB3, PREX2, and PTCH1, were also prevalent in the Catalog of Somatic Mutations in Cancer (COSMIC)42 Tier 1. COSMIC collects genes contributing to tumor promotion and directly accounts for clinical PSC cell therapy risks (second and fourth panels in Fig. 1g). To further assess tumorigenesis potential in P3 and P4 hESCs, we scored the impact of missense SNVs in genes within the TP53 network (T score) or COSMIC Tier 1 (C score) based on the Cancer Cell Line Encyclopedia (CCLE; Fig. 1h). The assessment revealed high tumorigenesis potential in P3 and P4 hESCs, as indicated by their high T- and C-scores, where the closer the score was to 1, the greater the impact was (Fig. 1h).
TP53 mutations relative to an increase in somatic mutations
Recent studies have highlighted the incidence of dominant-negative mutations in TP53 within hPSCs15. Since p53 is pivotal in inducing cell death in hPSCs8,43, p53 stabilization markedly promotes this outcome. Thus, with passaging, clonal dominance is readily achieved in TP53 mutant clones14. Two somatic missense SNVs, c.524 G > A (p.Arg175His for P3 and P4 hESCs) and c.785 G > C (p.Gly262Ala for P3 hESCs), were found in the TP53 DNA-binding domain (DBD), where most TP53 mutations develop in hPSCs14 (Fig. 2a). Remarkably, a missense SNV, c.524 G > A, predicted p53 structural damage (Fig. 2a). As previously described15, when mixed with P1 hESCs, P4 hESCs with TP53 mutations became dominant clones soon after treatment with Nutlin-3a (for inducing p53-dependent cell death) (Fig. 2b). This result corroborates the observation that p53 protein levels were markedly stabilized in P3 and P4 hESCs even without stimuli (Fig. 2c), with no significant TP53-dependent gene upregulation (i.e., GADD45A, PPM1D, or MDM2) (Fig. 2d). These findings demonstrate that TP53 mutations in P3 and P4 hESCs were dominant-negative.
a The identification of somatic missense SNVs in TP53 of P3 and P4 hESCs and the structural changes introduced by the missense SNV (R175H). Structural changes in TP53 were predicted via Missense3D. b Normal H9 cells tagged with eGFP and p53 mutant H9 were mixed and cultured. Nutlin-3a, a p53 activator, was added for 24 h, after which the cell populations were assayed and analyzed via flow cytometry. c Basal p53 protein levels in P1, P2, P3 and P4. d Expression levels of the p53 downstream genes GADD45A, PPM1D and MDM2. RNA-Seq for P1, P2, P3, and P4 hESCs was performed, and gene expression levels were quantified via RSEM with transcripts per million mapped reads (TPM) values. e p53 protein levels in established TP53KO cells compared with those in p53 normal H9 cells were determined by immunoblotting after 16 h of 1 µM Nutlin-3a treatment. f Resistance of TP53KO H9 cells to the p53 activator Nutlin-3a compared with that of the isogenic Mock pair. Nutlin-3a (2 µM) was added for 24 h. g The number of somatic SNVs identified in exons of P1-derived TP53 KO hESCs. In Fig. 2G, untranslated regions (UTRs) encompass both the 5′ and 3′ ends, and the functional effects include missense, nonsense, and splice sites. h The chromosomal distribution of somatic SNVs in P2-, P3-, P4-, and P1-derived TP53-KO hESCs. The abundance of somatic SNVs is presented as a heatmap. i Functional pathway annotation for genes with somatic noncoding SNVs in P2, P3, P4, and P1-derived TP53 KO hESCs. The functional pathways involved in the survival and proliferation of stem cells related to culture adaptation were selectively analyzed via Qiagen Ingenuity Pathway Analysis. j Gene Ontology (GO) enrichment analysis of downregulated genes in TP53-KO hESCs compared with wild-type (WT or P1) hESCs. The GO enrichment results were performed and validated as described in the methods section.
In the following STRING network analysis44, numerous somatic mutations were identified in genes linked to mutated TP53 in P3 and P4 hESCs (Supplementary Fig. 2a). This discovery suggests that mutated TP53 may influence mutations in other gene nodes within the TP53 network and increase the number of somatic mutations in P3 and P4 hESCs (Fig. 1d). Considering that p53 is integral for genome guidance45, we theorized that dominant-negative TP53 mutations favor the accumulation of somatic mutations, as depicted in Supplementary Fig. 2a. Therefore, we produced TP53 knockout hESCs (KO hESCs) from P1 hESCs (normal hESCs) by introducing an indel (insertion and deletion) in exon 4 with Cas9 (Supplementary Fig. 2b). Clonal selection was performed after Cas9 was used to establish TP53-KO hESCs (KO hESCs) with one base pair (bp) insertion to induce frameshift mutation (Supplementary Fig. 2c). Functional p53 KO was verified by the lack of p53 protein (Fig. 2e) and clear survival after Nutlin-3a treatment in KO hESCs (Fig. 2f).
Through WGS, we identified more exonic somatic mutations in KO hESCs than in P2 hESCs (Fig. 2g). Functional effect variants, including missense, nonsense, and splice-site SNVs (Supplementary Fig. 2d), were 1.6 times more abundant. The analysis also revealed a predominant distribution of somatic mutations on KO hESC chromosome 1, similar to P3 and P4 hESCs (Fig. 2h). This correlation suggests that TP53 mutations result in the accumulation of somatic mutations on specific chromosomes. Although most somatic mutations were identified in noncoding KO sequences, we conducted a functional annotation related to cell differentiation for genes with these mutations. The analysis revealed frequent gene mutations associated with functions such as apoptosis, cell polarity, cell adhesion, adipogenesis regulation, stemness, cytoskeletal development, and chromatin modification, which may be involved in the acquisition of hESC survival traits (Fig. 2i). In particular, genes related to apoptosis and growth were downregulated in TP53 KO cells (Fig. 2j). We also identified numerous somatic mutations in gene nodes within the TP53 KO network (Supplementary Fig. 2e), indicating somatic mutation expansion and accumulation within the TP53 network. A gain event with abnormal copy number changes throughout chromosome 1, with a significant copy number ratio, was also identified (Supplementary Fig. 2f, g). In contrast to P3 and P4 hESCs, TP53 KO hESCs did not exhibit CNV at 17q24.1/2 or 20q11.21 (Supplementary Fig. 2h). This result demonstrates that TP53 KO has undergone significant genetic alterations, affecting genetic instability.
Culture-adapted hESC cellular heterogeneity and transcriptome profiles
Despite the correlation between TP53 mutations and increased somatic mutations, TP53 mutations did not account for the 20q11.21 CNV gain and highly representative gene expression (TPX2 and BCL2L1) to trigger typical cellular events for “culture adaptation” (i.e., abnormal mitosis and survival traits; data not shown) and further genetic aberrations (i.e., additional CNVs such as 17q24 gain), as shown in Supplementary Fig. 2h (Fig. 3a). To monitor the variations in long-term hESC cultures, transcriptome profiles from P1, P2, P3, and P4 hESCs were obtained at the single-cell level (Fig. 3b). The cellular divergence of P3 (Clusters 1, 6, and 7) and P4 (Clusters 2, 6, and 9) hESCs from P1 and P2 hESCs (Clusters 0, 3, 4, 5, and 8) was identified via UMAP clustering analysis (Fig. 3b). The gene ontology (GO) annotation for genes highly expressed in the P3 and P4 hESC clusters revealed a unique function associated with long-term culture adaptation. Gene signatures of the apoptotic process related to negative regulation were consistently altered in the P3 and P4 hESC clusters. Similarly, ‘sterol biosynthesis’ was significantly enriched in P4 hESC clusters (Fig. 3c). The unique enriched gene set in P4 hESCs compared with that in P3 hESCs implied that further variation from P3 hESCs would lead to the acquisition of unique P4 hESC characteristics. The RNA velocity analysis to determine each cluster’s trajectory supported this hypothesis (Fig. 3d). Intriguingly, the expression of genes at the 20q11.21 locus, which consists of 46 genes, was relatively increased in all P3 and P4 hESC clusters (Fig. 3e). The expression levels of BCL2L1, KIF3B, HM13, TM9SF4 and COMMD7, which are involved in cell proliferation, differentiation inhibition, and anti-apoptosis46, at 20q11.21 were markedly elevated in P3 and P4 hESCs (Fig. 3f), indicating a potent selective advantage in culture.
a Scheme of isogenic pair hESCs and single-cell analysis considering the TP53 mutation status. b UMAP plot visualization of cultured hESCs (P1, P2, P3, and P4) colored according to 10 different transcriptionally distinct clusters (left panel); CL0, CL3, CL4, CL5 and CL8 for both P1 and P2 hESCs; CL1 and CL7 for P3 hESCs; CL2 and CL9 for P4 hESCs; and CL6 for both P3 and P4 hESCs. The right panel in Fig. 3b represents the composition of the clusters in each sample and the proportion of cells within each cluster. c GO terms enriched for each cluster based on differentially expressed genes. GO enrichment analysis was conducted via DAVID with a cutoff EASE score < 0.01. d Single-cell trajectory reconstructed by Monocle 3 for the four cultured hESCs. The trajectory indicates that P3 and P4 hESCs generated significant cellular heterogeneity from P1/P2 hESCs. Even between P3 and P4 hESCs, a high level of cellular divergence is shown. e Expression levels of 46 genes at 20q11.21 are indicated in each cell projected on the UMAP plot via the feature plot function. f Expression levels of BCL2L1, KLF3B, HM13, COMMD7, and TM9SF4 at 20q11.21 are indicated in each cell projected on the UMAP plot via the feature plot function. The VlnPlot below the UMAP plot shows the distribution of single-cell gene expression in each cluster. The y-axis of each panel represents the expression levels of the indicated genes. g Expression of CHCHD2 in Clusters 0, 3, 4, 5, and 8 for P1 and P2 hESCs. h Graphical representation of distinct changes in P3 and P4 hESCs, as determined by scRNA-seq (shown in red).
One of the most distinct differences in gene expression between clusters of normal hESCs (Clusters 0, 3, 4, 5, and 8) and the variants (Clusters 1, 6, and 7 for P3; Clusters 2, 6, and 9 for P4) was the expression of CHCHD2 (Fig. 3g). Notably, CHCHD2 is sharply repressed in hESCs with a gain of 20q11.2147 or after frequent exposure to a ROCK inhibitor41. The active transcription at the 20q11.21 locus (Fig. 3f) and the distinct repression pattern of CHCHD2 (Fig. 3g) indicate that the isogenic set of hESCs represents culture-adapted characteristics, at least at the transcriptome level (Fig. 3h).
Alterations in chromatin accessibility in culture-adapted variants
The survival traits and BCL2L1 expression evident in variants were not replicated in early-passaged iPSCs carrying the 20q11.21 gain21. Transcriptome profiles of iPSCs obtained from the Korea National Institute of Health (KNIH) (eight normal, four with 20q11.21 gain, including four iPSCs previously reported21 (Supplementary Fig. 3a) reinforced this consistency. Notably, the 20q11.21 gain in these iPSCs did not induce the expression of genes, such as HM13, ID1, BCL2L1, and TPX2, at the 20q11.21 locus (Supplementary Fig. 3b). In addition, two early-passaged hESC lines (KR1 and KR2: WA09 hESCs) from two independent laboratories at the Korea Research Institute Bioscience and Biotechnology (KRIBB) were examined. Intriguingly, the KR2 line with a 20q11.21 gain (Supplementary Fig. 3c) did not exhibit BCL2L1 induction (Supplementary Fig. 3d) or resistance to YM155 or nocodazole (Noc) treatment (Supplementary Fig. 3e). Furthermore, the KR2 line maintained an intact TP53 status (Supplementary Fig. 3f).
According to the scRNA data indicating active gene expression at 20q11.21 in later passages (Fig. 3e, f), an additional molecular event, such as epigenetic alterations22,48, could transcriptionally activate genes at this locus during in vitro culture. This event may directly induce the abnormal phenotypes of ‘culture-adapted variants’ (i.e., survival traits or abnormal mitosis) (Fig. 4a). Therefore, single-cell ATAC-seq was performed to monitor chromatin accessibility at the single-cell level. Major populations depending on these passages were distinctly clustered with varying chromatic accessibility. However, some minor populations existed in the overall distribution (Fig. 4b). Further classification revealed nine subclusters (Fig. 4c), and hESCs with different passage numbers formed their own major cluster (CL): CL2 in P1, CL3 in P2, CL1 in P3, and CL0 in P4.
a Scheme of chromatin structural alterations associated with culture-adapted variants. b Uniform manifold approximation and projection (UMAP) of scATAC-seq data from all the samples from P1 to P4. Each dot in the scatter plot represents single-cell chromatin accessibility. The four samples are color coded. c Clustering analysis based on chromatin accessibility. All the cells are grouped into 9 clusters and marked with numbers from 0 to 8. d The bar plot represents the proportion of a cluster for each sample. The major clusters are CL2 for P1, CL3 for P2, CL1 for P3, and CL0 for P4. These major clusters account for 80.6% of all cells. e Violin plots showing average open chromatin levels of differentially accessible regions (DARs) in major clusters. The y-axis represents the scaled average open chromatin levels of the DARs, which was calculated via the AddModuleScore() function. All DARs were clustered via k-means clustering with k = 10. The number of peaks belonging to a specific DAR are indicated. f–h Representative DARs corresponding to passages and Gene Ontology analysis of their target genes. UMAP shows the average level of open chromatin accessibility of the indicated DARs. The bar plot represents the top 5 Gene Ontology (GO) terms associated with putative target genes of the indicated DARs. The GO terms related to spindle assembly are marked in red. The p values of the enrichment were calculated via Metascape.
Overall, 567 differentially accessible regions (DARs) were identified in these major clusters. K-means clustering (k = 10) grouped all DARs into ten distinct clusters (Fig. 4d). For example, DAR2 and DAR4 chromatin accessibility was predominantly increased in major clusters of culture-adapted variants (i.e., P3 and P4). Thus, potential target genes were selected via associated cis-coaccessibility network (CCAN) analysis to investigate genes regulated by each DAR and their associations with specific phenotypes or cellular processes (Supplementary Fig. 4a). Similar to a previous report on methylation at the CHCHD2 promoters in the variants41, the lack of an scATAC-seq profile at the CHCHD2 promoter validated this analysis (Supplementary Fig. 4b). In this context, DARs were categorized into a total of 10 groups (Fig. 4e). DAR2 (Fig. 4f), 4, (Fig. 4g) and 5 (Fig. 4h) were generally associated with spindle assembly, whereas DAR6 (Supplementary Fig. 4c), 8 (Supplementary Fig. 4d), and 9 (Supplementary Fig. 4e) were frequently localized in normal hESCs (i.e., P1 and P2) without common gene ontology terms. In sharp contrast, DAR2 (primarily in P4), DAR4 (P3), and DAR5 (P3 and P4) were strongly associated with ‘spindle organization’.
These results suggest that an epigenetic change that alters chromatin structure-regulating genes associated with ‘spindle organization’ is induced in culture-adapted variants (i.e., P3 and P4). Consistently, aberrant mitosis with a lagging chromosome or chromosome bridge that results from abnormal spindle dynamics occurs in these culture-adapted variants18.
Epigenetic alteration affects genetic alterations in terms of gene expression
We hypothesized that a molecular event to activate the chromatin structure of the 20q11.21 locus would transpire in hPSCs along with a 20q11.21 gain. This event would also promote the expression of crucial genes, such as BCL2L1 (for survival) or TPX2 (for spindle stabilization and abnormal mitosis), to achieve the typical cellular phenotypes of culture-adapted variants (i.e., survival traits and abnormal mitosis)11,12,18 (Fig. 5a). To verify this theory, subsequent studies were conducted to analyze the chromatin structure of loci that presented increased DNA copy numbers.
a Scheme of phenotypic variations driven by genetic and epigenetic alterations in culture-adapted cells. b ATAC-seq signal along with CNV around the 20q11.21 locus. The dots and lines represent the log2-fold changes against P1 in terms of the copy number and ATAC-seq read count, respectively. c Heatmaps representing the pattern of the log2-fold change in the ATAC-seq signal around all the peaks found at 20q11.21. The fold changes were calculated against the P1 major cluster (CL2). The line plots show the average log2-fold change around peaks found at the same genomic loci. Each region (Regions 1, 2, and 3) was separated by dotted lines. d Comparative analysis of similarities between CNV and RNA and between ATAC and RNA. The heatmaps indicate log2-fold changes in CNV, ATAC and RNA for each gene located at 20q11.21. The dot size represents the similarity score calculated by the inverse of the distance. The significance of the similarity score difference was calculated via paired t tests. e A representative scATAC-seq profile at the BCL2L1 and TPX2 loci. The y-axis value represents the normalized ATAC-seq read count. The DAR peaks are marked by color boxes and numbered. The location and direction of genes are shown at the bottom. f, g Pearson correlation of the transcription factor (TF) activity score (chromVAR) of the transcription factor and open chromatin level at the peak. TF binding motifs are ordered by their correlation values. The TEAD family of TF binding motifs was most highly correlated with the BCL2L1 locus [Fig. 5e (1) ~ (5)]. The TF with the highest correlation at the TPX2 locus [Fig. 5e (6)] was identified as TEAD1.
Open chromatin levels exhibit dynamic changes across the genome, but these changes are dependent on CNV changes in regions with significant variations (e.g., 20q11.21 in P3 and P4; 17q24.1 and 17q24.2 in P4; Fig. 5b, Supplementary Fig. 5b, c). This observation may be attributed to the nature of the ATAC-seq technique, which reads DNA derived from open chromatin regions and enables the measurement of chromatin accessibility. However, when the average open chromatin around the peaks at 20q11.21 was examined, three distinct patterns were observed (Fig. 5c): Pattern 1 (Region 1, chr20:30,400,000-31,502,846) presented a significant increase in open chromatin at the peak center; Pattern 2 (Region 2, chr20:31,502,846-32,804,612) presented an overall increase in open chromatin around ±2 kb of the peak center; and Pattern 3 (Region 3, chr20:32,804,612-33,500,000) presented no changes in CNV and open chromatin.
The broad increase in open chromatin observed approximately ±2 kb from the peak center in Region 2 is attributable to DNA copy number amplification. In contrast, the localized increase in open chromatin at the peak center suggests additional epigenetic alterations. Notably, genes located in Region 2 presented increased open chromatin spanning their gene bodies, with a further increase in open chromatin downstream of 1 kb from the transcription start site (TSS) (Supplementary Fig. 5a). This finding implies that gene copy amplification, including regulatory sequences, and additional downstream epigenetic alterations were apparent at the promoter region. Therefore, changes in CNV patterns, open chromatin, and gene expression were compared across protein-coding genes within the 20q11.21 locus to investigate the importance of downstream genetic and epigenetic alterations in gene expression (Fig. 5d).
Although there were individual gene-specific differences, patterns of changes in RNA expression in Regions 1 and 2 closely resembled the pattern of open chromatin changes rather than CNV changes. This observation suggests that gene expression alterations at the 20q11.21 locus are likely due to upstream epigenetic alterations. On the other hand, with respect to the 17q24 locus, chromatin structure alterations did not resemble RNA expression variations compared with copy number changes. (Supplementary Fig. 5d, e). This scenario implies that DNA copy number alterations drive chromatin structure changes at the 17q24 locus.
Next, Tn5 footprinting was performed within each cluster, and the TF binding motifs in the footprints were examined to identify the transcription factors (TFs) involved in BCL2L1 and TPX2 expression. While the loss of the CHCHD2-specific peak was evident in P3 and P4 hESCs (Supplementary Fig. 5f), the peaks were markedly elevated in the BCL2L1 and TPX2 regions (Fig. 5e). The Pearson correlation coefficient (PCC) between the number of open chromatin molecules at each peak location and the TF activity score in each cell was calculated at the single-cell level. The higher the correlation coefficient was, the greater the level of involvement of open chromatin at the peak location in the TF activity calculation, suggesting a greater probability that the TF is actively binding at the peak. Among various TF candidates, the TEAD family exhibited a strong correlation between open chromatin and TF activity at the promoters of BCL2L1 (Fig. 5f) and TPX2 (Fig. 5g). Moreover, the indicative power of the TEAD family activity score increased at P3 and P4 (Supplementary Fig. 5g). Consistently, a distinct increase in TEAD4 protein levels was observed in P3 and P4, alongside BCL-xL protein induction (Supplementary Fig. 5h). These findings suggest that the TEAD family of TFs, consistent with YAP activation (or the loss of Hippo signaling) in culture-adapted variants17,35,40, may be pivotal in achieving ‘culture-adapted phenotypes’.
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- Source: https://www.nature.com/articles/s12276-024-01334-8